• DocumentCode
    577448
  • Title

    Back propagation neural network based on artificial bee colony algorithm

  • Author

    Jin, Feihu ; Shu, Guang

  • Author_Institution
    Department of Computing and Science, Harbin University of Science and Technology, Harbin China
  • fYear
    2012
  • fDate
    18-21 Sept. 2012
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    The artificial bee colony algorithm is a novel simulated evolutionary algorithm. The artificial bee colony algorithm has positive feedback, distributed computation and a constructive greedy heuristic convergence. Back propagation is a kind of feed forward neural network widely used in many areas, but it has some shortcomings, such as low precision solutions, slow search speed and easy convergence to the local minimum. The combination of artificial bee colony algorithm and back propagation neural network is adopted so that a nonlinear model can be identified and an inverted pendulum can be controlled. Simulation results show that the extensive mapping ability of neural network and the rapid global convergence of artificial bee colony algorithm can be obtained by combining artificial bee colony algorithm and neural network.
  • Keywords
    backpropagation; convergence; distributed control; evolutionary computation; feedback; feedforward; neural nets; nonlinear systems; ABC algorithm; BP algorithm; artificial bee colony algorithm; back propagation neural network; constructive greedy heuristic convergence; distributed computation; feed forward neural network; feedback; inverted pendulum control; nonlinear model; simulated evolutionary algorithm; Algorithm design and analysis; Approximation algorithms; Convergence; Heuristic algorithms; Neural networks; Sociology; Training; artificial bee colony; inverted pendulum system; neural network; system identification;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Strategic Technology (IFOST), 2012 7th International Forum on
  • Conference_Location
    Tomsk
  • Print_ISBN
    978-1-4673-1772-6
  • Type

    conf

  • DOI
    10.1109/IFOST.2012.6357623
  • Filename
    6357623